Development and implementation of a regional intensive care health service model
Bibliographic record
Abstract
Purpose The purpose of this paper is to present a case study of a healthcare service redesign. Before 1998, five community hospitals in Winnipeg each managed their intensive care units (ICUs) independently, providing virtually no access to patients in rural and remote regions of the province of Manitoba; and two tertiary university affiliated hospitals were left with insufficient intensive care beds to service the rest of the provincial population in addition to their tertiary service responsibilities. The authors resolved to create a city‐wide integrated critical care services model, in order to improve patient access, quality of care and cost effectiveness. Design/methodology/approach A population demand analysis was performed and service objectives were defined. A gap analysis became the basis of an integrated service model design and an implementation plan was formulated. Findings Beds were redistributed among community hospital ICUs to match available nursing resources. A credentialing process was developed to establish medical competency for attending physicians. A central bed registry and a referral triage system were implemented, to ensure that any Manitoban requiring an ICU admission acquired an appropriate bed in a timely manner. A regional computerized critical care database was introduced to all ICUs. The total number of beds was reduced from 92 to 84 and total occupancy fell from 65 to 58. The new model was entirely funded from bed reductions. Originality/value This paper describes the integration of a group of hospital‐based ICUs into a regional service delivery model developed to meet the needs of a provincial population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".